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8 relations: Bayesian inference, Bayesian structural time series, Gibbs sampling, Just another Gibbs sampler, Markov chain Monte Carlo, OpenBUGS, Spike-and-slab variable selection, WinBUGS.
Bayesian inference
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available.
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Bayesian structural time series
Bayesian structural time series (BSTS) model is a machine learning technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other.
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Gibbs sampling
In statistics, Gibbs sampling or a Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm for obtaining a sequence of observations which are approximated from a specified multivariate probability distribution, when direct sampling is difficult.
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Just another Gibbs sampler
Just another Gibbs sampler (JAGS) is a program for simulation from Bayesian hierarchical models using Markov chain Monte Carlo (MCMC), developed by Martyn Plummer.
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Markov chain Monte Carlo
In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution.
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OpenBUGS
OpenBUGS is a software application for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods.
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Spike-and-slab variable selection
Spike-and-slab regression is a Bayesian variable selection technique that is particularly useful when the number of possible predictors is larger than the number of observations.
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WinBUGS
WinBUGS is statistical software for Bayesian analysis using Markov chain Monte Carlo (MCMC) methods.
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BUGs (statistics), Bayesian inference Using Gibbs sampling.
References
[1] https://en.wikipedia.org/wiki/Bayesian_inference_using_Gibbs_sampling
